int lpx_mip_status(glp_prob *lp)
{ /* retrieve status of MIP solution */
int status;
switch (glp_mip_status(lp))
{ case GLP_UNDEF: status = LPX_I_UNDEF; break;
case GLP_OPT: status = LPX_I_OPT; break;
case GLP_FEAS: status = LPX_I_FEAS; break;
case GLP_NOFEAS: status = LPX_I_NOFEAS; break;
default: xassert(lp != lp);
}
return status;
}
int CMyProblem::PrintMIPSolution(ostream &out)
{
glp_create_index(lp);
out << "MIP solution" << endl;
out << "Dir;" << ((glp_get_obj_dir(lp)==GLP_MIN) ? "min" : "max") << endl;
out << "f;" << glp_mip_obj_val(lp) << endl;
out << "Status;" << DecodeStatus(glp_mip_status(lp)) << endl;
PrintSolArray(lp,"x",out,true);
PrintSolArray(lp,"y",out,true);
glp_delete_index(lp);
return 0;
}
static PyObject* LPX_getstatus(LPXObject *self, void *closure) {
int status;
switch (self->last_solver) {
case -1:
case 0: status=glp_get_status(LP); break;
case 1: status=glp_ipt_status(LP); break;
case 2: status=glp_mip_status(LP); break;
default:
PyErr_SetString(PyExc_RuntimeError,
"bad internal state for last solver identifier");
return NULL;
}
return glpstatus2string(status);
}
int main()
{
// Variáveis auxiliares
int i, j, constraintNumber, *constraintIndices;
double *constraintCoefficients;
// Aloca os vetores utilizados para criar as restrições do problema
// ***********************************************************************************************
// ATENÇÃO ===> É importante dizer que estes vetores serão utilizados da posição 1 em diante
// Ou seja, no GLPK você aloca uma posição a mais e descarta a posição 0 dos vetores.
// ***********************************************************************************************
constraintIndices = (int*)malloc((n+1)*sizeof(int));
constraintCoefficients = (double*)malloc((n+1)*sizeof(double));
// Cria um modelo com nenhuma variável e nenhuma restrição
glp_prob *model = glp_create_prob();
// Define o sentido da otimização que, para este problema, é minimização
glp_set_obj_dir(model, GLP_MIN);
// Cria as variáveis (colunas) no modelo
// Para este problema são necessárias n*n variáveis
// Estas n*n variáveis são definidas pelo GLPK através dos indices que vão de 1 até n*n (x1, x2, ..., x(n*n))
// Portanto, neste momento é importante determinar qual variável no GLPK (índice) representará qual variável x[i,j]
// Para tanto, fazemos o mapeamento das variáveis x[i,j] utilizando a fórmula (i-1)*n + j
// Isto é, a variável x[i,j] será representada pela variável de índice (i-1)*n + j no modelo do GLPK
// Note que é imprescindível que cada índice (variável do GLPK) seja associado a no máximo uma variável x[i,j]
// Caso contrário, uma variável do GLPK pode representar duas variáveis x[i,j] diferentes que assumem valores distintos nas soluções ótimas
// Neste caso, o modelo estará incorreto
glp_add_cols(model, n*n);
// Ajuste dos tipos, limitantes e coeficientes da função objetivo das variáveis do modelo
for (i = 1; i <= n; i++)
for (j = 1; j <= n; j++)
{
// Define o tipo da variável como sendo binária (em outros modelos poderia ser contínua (GLP_CV) ou inteira (GLP_IV))
glp_set_col_kind(model, (i-1)*n + j, GLP_BV);
// Define o limitante inferior (0) e superior (1) da variável
// Consultem no manual as outras forma para definir apenas o limitante inferior ou superior
glp_set_col_bnds(model, (i-1)*n + j, GLP_DB, 0.0, 1.0);
// Define o coeficiente da variável na função objetivo
glp_set_obj_coef(model, (i-1)*n + j, c[i-1][j-1]);
}
// Cria no modelo 2n restrições (linhas) nulas (com os coeficientes e limitantes zerados)
// Ou seja, neste momento é criada uma matriz de zeros que correspondem aos coeficientes das restrições do modelo
// O próximo passo será modificar esta matriz de tal forma que ela represente as restrições do problema descrito
glp_add_rows(model, 2*n);
// Esta variável define qual das restrições (qual linha da matriz de coeficientes) estamos modificando
// ************************************************************************************************************
// ATENÇÃO: perceba que as restrições (linhas), assim como as variáveis (colunas), são indexadas a partir de 1.
// ************************************************************************************************************
constraintNumber = 1;
// Preenchimento das restrições limitando a soma das linhas:
// sum{j in 1..n} w[i,j]*x[i,j] <= u[i] para i in 1..n
for (i = 1; i <= n; i++)
{
// Define o limite superior (RHS) da restrição
glp_set_row_bnds(model, constraintNumber, GLP_UP, 0.0, u[i-1]);
for (j = 1; j <= n; j++)
{
// Ajusta o índice da variável que será informado à rotina do GLPK
constraintIndices[j] = (i-1)*n + j;
// Ajusta o coeficiente da variável cujo índice foi definido na linha anterior para ser informado ao GLPK
// ******************************************************************************************************
// ATENÇÃO: perceba que na matriz w os índices e colunas são indexados a partir de ZERO !
// ******************************************************************************************************
constraintCoefficients[j] = w[i-1][j-1];
}
// Passa ao GLPK a restrição que acabou de ser definida nos vetores constraintIndices e constraintCoefficients
glp_set_mat_row(model, constraintNumber, n, constraintIndices, constraintCoefficients);
// atualiza o indice da próxima restrição a ser inserida
constraintNumber++;
}
// Preenchimento das restrições limitando a soma das colunas:
// sum{i in 1..n} w[i,j]*x[i,j] >= l[i] para j in 1..n
for (j = 1; j <= n; j++)
{
// Define o limite inferior (RHS) da restrição
glp_set_row_bnds(model, constraintNumber, GLP_LO, l[j-1], 0.0);
for (i = 1; i <= n; i++)
{
// Ajusta o índice da variável que será informado a rotina do GLPK
constraintIndices[i] = (i-1)*n + j;
// Ajusta o coeficiente da variável cujo índice foi definido na linha anterior para ser informado ao GLPK
constraintCoefficients[i] = w[i-1][j-1];
}
// Passa ao GLPK a restrição que acabou de ser definida nos vetores constraintIndices e constraintCoefficients
glp_set_mat_row(model, constraintNumber, n, constraintIndices, constraintCoefficients);
// atualiza o indice da próxima restrição a ser inserida
constraintNumber++;
}
// Define os parâmetros que serão passados ao resolvedor
glp_iocp param;
glp_init_iocp(¶m);
// Ativa o presolver
param.presolve = GLP_ON;
// Resolve o modelo
int status = glp_intopt(model, ¶m);
// Verifica se houve algum erro durante a otimização
if (status)
{
printf("Ocorreu um erro durante o processo de otimizacao.\n");
}
else
{
// Verifica se o método encontrou uma solução
status = glp_mip_status(model);
if ((status == GLP_OPT) || (status == GLP_FEAS))
{
// Imprime a solução encontrada
if (status == GLP_OPT)
printf("Solucao otima encontrada!\n");
else
printf("A solucao encontrada pode nao ser otima!\n");
printf("Custo da solucao: %f\n", glp_mip_obj_val(model));
for (i = 1; i <= n; i++)
{
for (j = 1; j <= n; j++)
printf("%f ", glp_mip_col_val(model, (i-1)*n + j));
printf("\n");
}
}
else
{
printf("Nenhuma solucao foi encontrada!\n");
}
}
// Desaloca os vetores
free(constraintIndices);
free(constraintCoefficients);
return 0;
}
/**
* Solves the MLP problem
*
* @param mlp the MLP Handle
* @param s_ctx context to return results
* @return GNUNET_OK if could be solved, GNUNET_SYSERR on failure
*/
int
mlp_solve_mlp_problem (struct GAS_MLP_Handle *mlp, struct GAS_MLP_SolutionContext *s_ctx)
{
int res;
struct GNUNET_TIME_Relative duration;
struct GNUNET_TIME_Absolute end;
struct GNUNET_TIME_Absolute start = GNUNET_TIME_absolute_get();
/* solve MLP problem */
res = glp_intopt(mlp->prob, &mlp->control_param_mlp);
if (res == 0)
{
/* The MLP problem instance has been successfully solved. */
}
else if (res == GLP_EITLIM)
{
/* simplex iteration limit has been exceeded. */
// TODO Increase iteration limit?
}
else if (res == GLP_ETMLIM)
{
/* Time limit has been exceeded. */
// TODO Increase time limit?
}
else
{
/* Problem was ill-defined, no way to handle that */
GNUNET_log_from (GNUNET_ERROR_TYPE_DEBUG,
"ats-mlp",
"Solving MLP problem failed: %s\n", mlp_solve_to_string(res));
return GNUNET_SYSERR;
}
end = GNUNET_TIME_absolute_get ();
duration = GNUNET_TIME_absolute_get_difference (start, end);
mlp->mlp_solved++;
mlp->mlp_total_duration =+ duration.rel_value;
s_ctx->mlp_duration = duration;
GNUNET_STATISTICS_update (mlp->stats,"# MLP problem solved", 1, GNUNET_NO);
GNUNET_STATISTICS_set (mlp->stats,"# MLP execution time (ms)", duration.rel_value, GNUNET_NO);
GNUNET_STATISTICS_set (mlp->stats,"# MLP execution time average (ms)",
mlp->mlp_total_duration / mlp->mlp_solved, GNUNET_NO);
/* Analyze problem status */
res = glp_mip_status(mlp->prob);
switch (res) {
/* solution is optimal */
case GLP_OPT:
/* solution is feasible */
case GLP_FEAS:
break;
/* Problem was ill-defined, no way to handle that */
default:
GNUNET_log_from (GNUNET_ERROR_TYPE_DEBUG,
"ats-mlp",
"Solving MLP problem failed, %s\n\n", mlp_status_to_string(res));
return GNUNET_SYSERR;
break;
}
return GNUNET_OK;
}
int glp_print_mip(glp_prob *P, const char *fname)
{ /* write MIP solution in printable format */
glp_file *fp;
GLPROW *row;
GLPCOL *col;
int i, j, t, ae_ind, re_ind, ret;
double ae_max, re_max;
xprintf("Writing MIP solution to '%s'...\n", fname);
fp = glp_open(fname, "w");
if (fp == NULL)
{ xprintf("Unable to create '%s' - %s\n", fname, get_err_msg());
ret = 1;
goto done;
}
xfprintf(fp, "%-12s%s\n", "Problem:",
P->name == NULL ? "" : P->name);
xfprintf(fp, "%-12s%d\n", "Rows:", P->m);
xfprintf(fp, "%-12s%d (%d integer, %d binary)\n", "Columns:",
P->n, glp_get_num_int(P), glp_get_num_bin(P));
xfprintf(fp, "%-12s%d\n", "Non-zeros:", P->nnz);
t = glp_mip_status(P);
xfprintf(fp, "%-12s%s\n", "Status:",
t == GLP_OPT ? "INTEGER OPTIMAL" :
t == GLP_FEAS ? "INTEGER NON-OPTIMAL" :
t == GLP_NOFEAS ? "INTEGER EMPTY" :
t == GLP_UNDEF ? "INTEGER UNDEFINED" : "???");
xfprintf(fp, "%-12s%s%s%.10g (%s)\n", "Objective:",
P->obj == NULL ? "" : P->obj,
P->obj == NULL ? "" : " = ", P->mip_obj,
P->dir == GLP_MIN ? "MINimum" :
P->dir == GLP_MAX ? "MAXimum" : "???");
xfprintf(fp, "\n");
xfprintf(fp, " No. Row name Activity Lower bound "
" Upper bound\n");
xfprintf(fp, "------ ------------ ------------- ------------- "
"-------------\n");
for (i = 1; i <= P->m; i++)
{ row = P->row[i];
xfprintf(fp, "%6d ", i);
if (row->name == NULL || strlen(row->name) <= 12)
xfprintf(fp, "%-12s ", row->name == NULL ? "" : row->name);
else
xfprintf(fp, "%s\n%20s", row->name, "");
xfprintf(fp, "%3s", "");
xfprintf(fp, "%13.6g ",
fabs(row->mipx) <= 1e-9 ? 0.0 : row->mipx);
if (row->type == GLP_LO || row->type == GLP_DB ||
row->type == GLP_FX)
xfprintf(fp, "%13.6g ", row->lb);
else
xfprintf(fp, "%13s ", "");
if (row->type == GLP_UP || row->type == GLP_DB)
xfprintf(fp, "%13.6g ", row->ub);
else
xfprintf(fp, "%13s ", row->type == GLP_FX ? "=" : "");
xfprintf(fp, "\n");
}
xfprintf(fp, "\n");
xfprintf(fp, " No. Column name Activity Lower bound "
" Upper bound\n");
xfprintf(fp, "------ ------------ ------------- ------------- "
"-------------\n");
for (j = 1; j <= P->n; j++)
{ col = P->col[j];
xfprintf(fp, "%6d ", j);
if (col->name == NULL || strlen(col->name) <= 12)
xfprintf(fp, "%-12s ", col->name == NULL ? "" : col->name);
else
xfprintf(fp, "%s\n%20s", col->name, "");
xfprintf(fp, "%s ",
col->kind == GLP_CV ? " " :
col->kind == GLP_IV ? "*" : "?");
xfprintf(fp, "%13.6g ",
fabs(col->mipx) <= 1e-9 ? 0.0 : col->mipx);
if (col->type == GLP_LO || col->type == GLP_DB ||
col->type == GLP_FX)
xfprintf(fp, "%13.6g ", col->lb);
else
xfprintf(fp, "%13s ", "");
if (col->type == GLP_UP || col->type == GLP_DB)
xfprintf(fp, "%13.6g ", col->ub);
else
xfprintf(fp, "%13s ", col->type == GLP_FX ? "=" : "");
xfprintf(fp, "\n");
}
xfprintf(fp, "\n");
xfprintf(fp, "Integer feasibility conditions:\n");
xfprintf(fp, "\n");
glp_check_kkt(P, GLP_MIP, GLP_KKT_PE, &ae_max, &ae_ind, &re_max,
&re_ind);
xfprintf(fp, "KKT.PE: max.abs.err = %.2e on row %d\n",
ae_max, ae_ind);
xfprintf(fp, " max.rel.err = %.2e on row %d\n",
re_max, re_ind);
xfprintf(fp, "%8s%s\n", "",
re_max <= 1e-9 ? "High quality" :
re_max <= 1e-6 ? "Medium quality" :
re_max <= 1e-3 ? "Low quality" : "SOLUTION IS WRONG");
xfprintf(fp, "\n");
glp_check_kkt(P, GLP_MIP, GLP_KKT_PB, &ae_max, &ae_ind, &re_max,
&re_ind);
xfprintf(fp, "KKT.PB: max.abs.err = %.2e on %s %d\n",
ae_max, ae_ind <= P->m ? "row" : "column",
ae_ind <= P->m ? ae_ind : ae_ind - P->m);
xfprintf(fp, " max.rel.err = %.2e on %s %d\n",
re_max, re_ind <= P->m ? "row" : "column",
re_ind <= P->m ? re_ind : re_ind - P->m);
xfprintf(fp, "%8s%s\n", "",
re_max <= 1e-9 ? "High quality" :
re_max <= 1e-6 ? "Medium quality" :
re_max <= 1e-3 ? "Low quality" : "SOLUTION IS INFEASIBLE");
xfprintf(fp, "\n");
xfprintf(fp, "End of output\n");
#if 0 /* FIXME */
xfflush(fp);
#endif
if (glp_ioerr(fp))
{ xprintf("Write error on '%s' - %s\n", fname, get_err_msg());
ret = 1;
goto done;
}
ret = 0;
done:
if (fp != NULL) glp_close(fp);
return ret;
}
static void
maybe_check_results(const int ppl_status, const double ppl_optimum_value) {
const char* ppl_status_string;
const char* glpk_status_string;
int glpk_status;
int treat_as_lp = 0;
glp_smcp glpk_smcp;
if (!check_results)
return;
if (no_mip || glpk_lp_num_int == 0)
treat_as_lp = 1;
glp_set_obj_dir(glpk_lp, (maximize ? GLP_MAX : GLP_MIN));
glp_init_smcp(&glpk_smcp);
/* Disable GLPK output. */
glpk_smcp.msg_lev = GLP_MSG_OFF;
if (treat_as_lp) {
/* Set the problem class to LP: MIP problems are thus treated as
LP ones. */
glp_exact(glpk_lp, &glpk_smcp);
glpk_status = glp_get_status(glpk_lp);
}
else {
/* MIP case. */
glp_simplex(glpk_lp, &glpk_smcp);
glpk_status = glp_get_status(glpk_lp);
if (glpk_status != GLP_NOFEAS && glpk_status != GLP_UNBND) {
glp_iocp glpk_iocp;
glp_init_iocp(&glpk_iocp);
/* Disable GLPK output. */
glpk_iocp.msg_lev = GLP_MSG_OFF;
glp_intopt(glpk_lp, &glpk_iocp);
glpk_status = glp_mip_status(glpk_lp);
}
}
/* If no_optimization is enabled, the second case is not possibile. */
if (!((ppl_status == PPL_MIP_PROBLEM_STATUS_UNFEASIBLE
&& glpk_status == GLP_NOFEAS)
|| (ppl_status == PPL_MIP_PROBLEM_STATUS_UNBOUNDED
&& glpk_status == GLP_UNBND)
|| (ppl_status == PPL_MIP_PROBLEM_STATUS_OPTIMIZED
&& (glpk_status == GLP_OPT
/* If no_optimization is enabled, check if the problem is
unbounded for GLPK. */
|| (no_optimization && (glpk_status == GLP_UNBND
|| glpk_status == GLP_UNDEF)))))) {
if (ppl_status == PPL_MIP_PROBLEM_STATUS_UNFEASIBLE)
ppl_status_string = "unfeasible";
else if (ppl_status == PPL_MIP_PROBLEM_STATUS_UNBOUNDED)
ppl_status_string = "unbounded";
else if (ppl_status == PPL_MIP_PROBLEM_STATUS_OPTIMIZED)
ppl_status_string = "optimizable";
else
ppl_status_string = "<?>";
switch (glpk_status) {
case GLP_NOFEAS:
glpk_status_string = "unfeasible";
break;
case GLP_UNBND:
glpk_status_string = "unbounded";
break;
case GLP_OPT:
glpk_status_string = "optimizable";
break;
case GLP_UNDEF:
glpk_status_string = "undefined";
break;
default:
glpk_status_string = "<?>";
break;
}
error("check failed: for GLPK the problem is %s, not %s",
glpk_status_string, ppl_status_string);
check_results_failed = 1;
}
else if (!no_optimization
&& ppl_status == PPL_MIP_PROBLEM_STATUS_OPTIMIZED) {
double glpk_optimum_value
= (treat_as_lp ? glp_get_obj_val(glpk_lp) : glp_mip_obj_val(glpk_lp));
if (fabs(ppl_optimum_value - glpk_optimum_value) > check_threshold) {
error("check failed: for GLPK the problem's optimum is %.20g,"
" not %.20g", glpk_optimum_value, ppl_optimum_value);
check_results_failed = 1;
}
}
return;
}
OptSolutionData* GLPKRunSolver(int ProbType) {
OptSolutionData* NewSolution = NULL;
int NumVariables = glp_get_num_cols(GLPKModel);
int Status = 0;
if (ProbType == MILP) {
Status = glp_simplex(GLPKModel, NULL); // Use default settings
if (Status != 0) {
FErrorFile() << "Failed to optimize problem." << endl;
FlushErrorFile();
return NULL;
}
Status = glp_intopt(GLPKModel, NULL); // Use default settings
if (Status != 0) {
FErrorFile() << "Failed to optimize problem." << endl;
FlushErrorFile();
return NULL;
}
NewSolution = new OptSolutionData;
Status = glp_mip_status(GLPKModel);
if (Status == GLP_UNDEF || Status == GLP_NOFEAS) {
NewSolution->Status = INFEASIBLE;
return NewSolution;
} else if (Status == GLP_FEAS) {
NewSolution->Status = UNBOUNDED;
return NewSolution;
} else if (Status == GLP_OPT) {
NewSolution->Status = SUCCESS;
} else {
delete NewSolution;
FErrorFile() << "Problem status unrecognized." << endl;
FlushErrorFile();
return NULL;
}
NewSolution->Objective = glp_mip_obj_val(GLPKModel);
NewSolution->SolutionData.resize(NumVariables);
for (int i=0; i < NumVariables; i++) {
NewSolution->SolutionData[i] = glp_mip_col_val(GLPKModel, i+1);
}
} else if (ProbType == LP) {
//First we check the basis matrix to ensure it is not singular
if (glp_warm_up(GLPKModel) != 0) {
glp_adv_basis(GLPKModel, 0);
}
Status = glp_simplex(GLPKModel, NULL); // Use default settings
if (Status == GLP_EBADB) { /* the basis is invalid; build some valid basis */
glp_adv_basis(GLPKModel, 0);
Status = glp_simplex(GLPKModel, NULL); // Use default settings
}
if (Status != 0) {
FErrorFile() << "Failed to optimize problem." << endl;
FlushErrorFile();
return NULL;
}
NewSolution = new OptSolutionData;
Status = glp_get_status(GLPKModel);
if (Status == GLP_INFEAS || Status == GLP_NOFEAS || Status == GLP_UNDEF) {
cout << "Model is infeasible" << endl;
FErrorFile() << "Model is infeasible" << endl;
FlushErrorFile();
NewSolution->Status = INFEASIBLE;
return NewSolution;
} else if (Status == GLP_FEAS || Status == GLP_UNBND) {
cout << "Model is unbounded" << endl;
FErrorFile() << "Model is unbounded" << endl;
FlushErrorFile();
NewSolution->Status = UNBOUNDED;
return NewSolution;
} else if (Status == GLP_OPT) {
NewSolution->Status = SUCCESS;
} else {
delete NewSolution;
FErrorFile() << "Problem status unrecognized." << endl;
FlushErrorFile();
return NULL;
}
NewSolution->Objective = glp_get_obj_val(GLPKModel);
NewSolution->SolutionData.resize(NumVariables);
for (int i=0; i < NumVariables; i++) {
NewSolution->SolutionData[i] = glp_get_col_prim(GLPKModel, i+1);
}
} else {
FErrorFile() << "Optimization problem type cannot be handled by GLPK solver." << endl;
FlushErrorFile();
return NULL;
}
return NewSolution;
}
int glpk (int sense, int n, int m, double *c, int nz, int *rn, int *cn,
double *a, double *b, char *ctype, int *freeLB, double *lb,
int *freeUB, double *ub, int *vartype, int isMIP, int lpsolver,
int save_pb, char *save_filename, char *filetype,
double *xmin, double *fmin, double *status,
double *lambda, double *redcosts, double *time, double *mem)
{
int typx = 0;
int method;
clock_t t_start = clock();
// Obsolete
//lib_set_fault_hook (NULL, glpk_fault_hook);
//Redirect standard output
if (glpIntParam[0] > 1) glp_term_hook (glpk_print_hook, NULL);
else glp_term_hook (NULL, NULL);
//-- Create an empty LP/MILP object
glp_prob *lp = glp_create_prob ();
//-- Set the sense of optimization
if (sense == 1)
glp_set_obj_dir (lp, GLP_MIN);
else
glp_set_obj_dir (lp, GLP_MAX);
//-- Define the number of unknowns and their domains.
glp_add_cols (lp, n);
for (int i = 0; i < n; i++)
{
//-- Define type of the structural variables
if (! freeLB[i] && ! freeUB[i])
glp_set_col_bnds (lp, i+1, GLP_DB, lb[i], ub[i]);
else
{
if (! freeLB[i] && freeUB[i])
glp_set_col_bnds (lp, i+1, GLP_LO, lb[i], ub[i]);
else
{
if (freeLB[i] && ! freeUB[i])
glp_set_col_bnds (lp, i+1, GLP_UP, lb[i], ub[i]);
else
glp_set_col_bnds (lp, i+1, GLP_FR, lb[i], ub[i]);
}
}
// -- Set the objective coefficient of the corresponding
// -- structural variable. No constant term is assumed.
glp_set_obj_coef(lp,i+1,c[i]);
if (isMIP)
glp_set_col_kind (lp, i+1, vartype[i]);
}
glp_add_rows (lp, m);
for (int i = 0; i < m; i++)
{
/* If the i-th row has no lower bound (types F,U), the
corrispondent parameter will be ignored.
If the i-th row has no upper bound (types F,L), the corrispondent
parameter will be ignored.
If the i-th row is of S type, the i-th LB is used, but
the i-th UB is ignored.
*/
switch (ctype[i])
{
case 'F': typx = GLP_FR; break;
// upper bound
case 'U': typx = GLP_UP; break;
// lower bound
case 'L': typx = GLP_LO; break;
// fixed constraint
case 'S': typx = GLP_FX; break;
// double-bounded variable
case 'D': typx = GLP_DB; break;
}
glp_set_row_bnds (lp, i+1, typx, b[i], b[i]);
}
// Load constraint matrix A
glp_load_matrix (lp, nz, rn, cn, a);
// Save problem
if (save_pb) {
if (!strcmp(filetype,"cplex")){
if (lpx_write_cpxlp (lp, save_filename) != 0) {
mexErrMsgTxt("glpkcc: unable to write the problem");
longjmp (mark, -1);
}
}else{
if (!strcmp(filetype,"fixedmps")){
if (lpx_write_mps (lp, save_filename) != 0) {
mexErrMsgTxt("glpkcc: unable to write the problem");
longjmp (mark, -1);
}
}else{
if (!strcmp(filetype,"freemps")){
if (lpx_write_freemps (lp, save_filename) != 0) {
mexErrMsgTxt("glpkcc: unable to write the problem");
longjmp (mark, -1);
}
}else{// plain text
if (lpx_print_prob (lp, save_filename) != 0) {
mexErrMsgTxt("glpkcc: unable to write the problem");
longjmp (mark, -1);
}
}
}
}
}
//-- scale the problem data (if required)
if (glpIntParam[1] && (! glpIntParam[16] || lpsolver != 1))
lpx_scale_prob (lp);
//-- build advanced initial basis (if required)
if (lpsolver == 1 && ! glpIntParam[16])
lpx_adv_basis (lp);
glp_smcp sParam;
glp_init_smcp(&sParam);
//-- set control parameters
if (lpsolver==1){
//remap of control parameters for simplex method
sParam.msg_lev=glpIntParam[0]; // message level
// simplex method: primal/dual
if (glpIntParam[2]==0) sParam.meth=GLP_PRIMAL;
else sParam.meth=GLP_DUALP;
// pricing technique
if (glpIntParam[3]==0) sParam.pricing=GLP_PT_STD;
else sParam.pricing=GLP_PT_PSE;
//sParam.r_test not available
sParam.tol_bnd=glpRealParam[1]; // primal feasible tollerance
sParam.tol_dj=glpRealParam[2]; // dual feasible tollerance
sParam.tol_piv=glpRealParam[3]; // pivot tollerance
sParam.obj_ll=glpRealParam[4]; // lower limit
sParam.obj_ul=glpRealParam[5]; // upper limit
// iteration limit
if (glpIntParam[5]==-1) sParam.it_lim=INT_MAX;
else sParam.it_lim=glpIntParam[5];
// time limit
if (glpRealParam[6]==-1) sParam.tm_lim=INT_MAX;
else sParam.tm_lim=(int) glpRealParam[6];
sParam.out_frq=glpIntParam[7]; // output frequency
sParam.out_dly=(int) glpRealParam[7]; // output delay
// presolver
if (glpIntParam[16]) sParam.presolve=GLP_ON;
else sParam.presolve=GLP_OFF;
}else{
for(int i = 0; i < NIntP; i++)
lpx_set_int_parm (lp, IParam[i], glpIntParam[i]);
for (int i = 0; i < NRealP; i++)
lpx_set_real_parm (lp, RParam[i], glpRealParam[i]);
}
// Choose simplex method ('S') or interior point method ('T') to solve the problem
if (lpsolver == 1)
method = 'S';
else
method = 'T';
int errnum;
switch (method){
case 'S': {
if (isMIP){
method = 'I';
errnum = lpx_intopt (lp);
}
else{
errnum = glp_simplex(lp, &sParam);
errnum += 100; //this is to avoid ambiguity in the return codes.
}
}
break;
case 'T': errnum = lpx_interior(lp); break;
default: xassert (method != method);
}
/* errnum assumes the following results:
errnum = 0 <=> No errors
errnum = 1 <=> Iteration limit exceeded.
errnum = 2 <=> Numerical problems with basis matrix.
*/
if (errnum == LPX_E_OK || errnum==100){
// Get status and object value
if (isMIP)
{
*status = glp_mip_status (lp);
*fmin = glp_mip_obj_val (lp);
}
else
{
if (lpsolver == 1)
{
*status = glp_get_status (lp);
*fmin = glp_get_obj_val (lp);
}
else
{
*status = glp_ipt_status (lp);
*fmin = glp_ipt_obj_val (lp);
}
}
// Get optimal solution (if exists)
if (isMIP)
{
for (int i = 0; i < n; i++)
xmin[i] = glp_mip_col_val (lp, i+1);
}
else
{
/* Primal values */
for (int i = 0; i < n; i++)
{
if (lpsolver == 1)
xmin[i] = glp_get_col_prim (lp, i+1);
else
xmin[i] = glp_ipt_col_prim (lp, i+1);
}
/* Dual values */
for (int i = 0; i < m; i++)
{
if (lpsolver == 1) lambda[i] = glp_get_row_dual (lp, i+1);
else lambda[i] = glp_ipt_row_dual (lp, i+1);
}
/* Reduced costs */
for (int i = 0; i < glp_get_num_cols (lp); i++)
{
if (lpsolver == 1) redcosts[i] = glp_get_col_dual (lp, i+1);
else redcosts[i] = glp_ipt_col_dual (lp, i+1);
}
}
*time = (clock () - t_start) / CLOCKS_PER_SEC;
glp_ulong tpeak;
lib_mem_usage(NULL, NULL, NULL, &tpeak);
*mem=(double)(4294967296.0 * tpeak.hi + tpeak.lo) / (1024);
glp_delete_prob (lp);
return 0;
}
glp_delete_prob (lp);
*status = errnum;
return errnum;
}
int glpk (int sense, int n, int m, double *c, int nz, int *rn, int *cn,
double *a, double *b, char *ctype, int *freeLB, double *lb,
int *freeUB, double *ub, int *vartype, int isMIP, int lpsolver,
int save_pb, char *save_filename, char *filetype,
double *xmin, double *fmin, double *status,
double *lambda, double *redcosts, double *time, double *mem)
{
int typx = 0;
int method;
clock_t t_start = clock();
//Redirect standard output
if (glpIntParam[0] > 1) glp_term_hook (glpk_print_hook, NULL);
else glp_term_hook (NULL, NULL);
//-- Create an empty LP/MILP object
LPX *lp = lpx_create_prob ();
//-- Set the sense of optimization
if (sense == 1)
glp_set_obj_dir (lp, GLP_MIN);
else
glp_set_obj_dir (lp, GLP_MAX);
//-- Define the number of unknowns and their domains.
glp_add_cols (lp, n);
for (int i = 0; i < n; i++)
{
//-- Define type of the structural variables
if (! freeLB[i] && ! freeUB[i]) {
if ( lb[i] == ub[i] )
glp_set_col_bnds (lp, i+1, GLP_FX, lb[i], ub[i]);
else
glp_set_col_bnds (lp, i+1, GLP_DB, lb[i], ub[i]);
}
else
{
if (! freeLB[i] && freeUB[i])
glp_set_col_bnds (lp, i+1, GLP_LO, lb[i], ub[i]);
else
{
if (freeLB[i] && ! freeUB[i])
glp_set_col_bnds (lp, i+1, GLP_UP, lb[i], ub[i]);
else
glp_set_col_bnds (lp, i+1, GLP_FR, lb[i], ub[i]);
}
}
// -- Set the objective coefficient of the corresponding
// -- structural variable. No constant term is assumed.
glp_set_obj_coef(lp,i+1,c[i]);
if (isMIP)
glp_set_col_kind (lp, i+1, vartype[i]);
}
glp_add_rows (lp, m);
for (int i = 0; i < m; i++)
{
/* If the i-th row has no lower bound (types F,U), the
corrispondent parameter will be ignored.
If the i-th row has no upper bound (types F,L), the corrispondent
parameter will be ignored.
If the i-th row is of S type, the i-th LB is used, but
the i-th UB is ignored.
*/
switch (ctype[i])
{
case 'F': typx = GLP_FR; break;
// upper bound
case 'U': typx = GLP_UP; break;
// lower bound
case 'L': typx = GLP_LO; break;
// fixed constraint
case 'S': typx = GLP_FX; break;
// double-bounded variable
case 'D': typx = GLP_DB; break;
}
if ( typx == GLP_DB && -b[i] < b[i]) {
glp_set_row_bnds (lp, i+1, typx, -b[i], b[i]);
}
else if(typx == GLP_DB && -b[i] == b[i]) {
glp_set_row_bnds (lp, i+1, GLP_FX, b[i], b[i]);
}
else {
// this should be glp_set_row_bnds (lp, i+1, typx, -b[i], b[i]);
glp_set_row_bnds (lp, i+1, typx, b[i], b[i]);
}
}
// Load constraint matrix A
glp_load_matrix (lp, nz, rn, cn, a);
// Save problem
if (save_pb) {
if (!strcmp(filetype,"cplex")){
if (glp_write_lp (lp, NULL, save_filename) != 0) {
mexErrMsgTxt("glpk: unable to write the problem");
longjmp (mark, -1);
}
}else{
if (!strcmp(filetype,"fixedmps")){
if (glp_write_mps (lp, GLP_MPS_DECK, NULL, save_filename) != 0) {
mexErrMsgTxt("glpk: unable to write the problem");
longjmp (mark, -1);
}
}else{
if (!strcmp(filetype,"freemps")){
if (glp_write_mps (lp, GLP_MPS_FILE, NULL, save_filename) != 0) {
mexErrMsgTxt("glpk: unable to write the problem");
longjmp (mark, -1);
}
}else{// plain text
if (lpx_print_prob (lp, save_filename) != 0) {
mexErrMsgTxt("glpk: unable to write the problem");
longjmp (mark, -1);
}
}
}
}
}
//-- scale the problem data (if required)
if (! glpIntParam[16] || lpsolver != 1) {
switch ( glpIntParam[1] ) {
case ( 0 ): glp_scale_prob( lp, GLP_SF_SKIP ); break;
case ( 1 ): glp_scale_prob( lp, GLP_SF_GM ); break;
case ( 2 ): glp_scale_prob( lp, GLP_SF_EQ ); break;
case ( 3 ): glp_scale_prob( lp, GLP_SF_AUTO ); break;
case ( 4 ): glp_scale_prob( lp, GLP_SF_2N ); break;
default :
mexErrMsgTxt("glpk: unrecognized scaling option");
longjmp (mark, -1);
}
}
else {
/* do nothing? or unscale?
glp_unscale_prob( lp );
*/
}
//-- build advanced initial basis (if required)
if (lpsolver == 1 && ! glpIntParam[16])
glp_adv_basis (lp, 0);
glp_smcp sParam;
glp_init_smcp(&sParam);
//-- set control parameters for simplex/exact method
if (lpsolver == 1 || lpsolver == 3){
//remap of control parameters for simplex method
sParam.msg_lev=glpIntParam[0]; // message level
// simplex method: primal/dual
switch ( glpIntParam[2] ) {
case 0: sParam.meth=GLP_PRIMAL; break;
case 1: sParam.meth=GLP_DUAL; break;
case 2: sParam.meth=GLP_DUALP; break;
default:
mexErrMsgTxt("glpk: unrecognized primal/dual method");
longjmp (mark, -1);
}
// pricing technique
if (glpIntParam[3]==0) sParam.pricing=GLP_PT_STD;
else sParam.pricing=GLP_PT_PSE;
// ratio test
if (glpIntParam[20]==0) sParam.r_test = GLP_RT_STD;
else sParam.r_test=GLP_RT_HAR;
//tollerances
sParam.tol_bnd=glpRealParam[1]; // primal feasible tollerance
sParam.tol_dj=glpRealParam[2]; // dual feasible tollerance
sParam.tol_piv=glpRealParam[3]; // pivot tollerance
sParam.obj_ll=glpRealParam[4]; // lower limit
sParam.obj_ul=glpRealParam[5]; // upper limit
// iteration limit
if (glpIntParam[5]==-1) sParam.it_lim=INT_MAX;
else sParam.it_lim=glpIntParam[5];
// time limit
if (glpRealParam[6]==-1) sParam.tm_lim=INT_MAX;
else sParam.tm_lim=(int) glpRealParam[6];
sParam.out_frq=glpIntParam[7]; // output frequency
sParam.out_dly=(int) glpRealParam[7]; // output delay
// presolver
if (glpIntParam[16]) sParam.presolve=GLP_ON;
else sParam.presolve=GLP_OFF;
}else{
for(int i = 0; i < NIntP; i++) {
// skip assinging ratio test or
if ( i == 18 || i == 20) continue;
lpx_set_int_parm (lp, IParam[i], glpIntParam[i]);
}
for (int i = 0; i < NRealP; i++) {
lpx_set_real_parm (lp, RParam[i], glpRealParam[i]);
}
}
//set MIP params if MIP....
glp_iocp iParam;
glp_init_iocp(&iParam);
if ( isMIP ){
method = 'I';
switch (glpIntParam[0]) { //message level
case 0: iParam.msg_lev = GLP_MSG_OFF; break;
case 1: iParam.msg_lev = GLP_MSG_ERR; break;
case 2: iParam.msg_lev = GLP_MSG_ON; break;
case 3: iParam.msg_lev = GLP_MSG_ALL; break;
default: mexErrMsgTxt("glpk: msg_lev bad param");
}
switch (glpIntParam[14]) { //branching param
case 0: iParam.br_tech = GLP_BR_FFV; break;
case 1: iParam.br_tech = GLP_BR_LFV; break;
case 2: iParam.br_tech = GLP_BR_MFV; break;
case 3: iParam.br_tech = GLP_BR_DTH; break;
default: mexErrMsgTxt("glpk: branch bad param");
}
switch (glpIntParam[15]) { //backtracking heuristic
case 0: iParam.bt_tech = GLP_BT_DFS; break;
case 1: iParam.bt_tech = GLP_BT_BFS; break;
case 2: iParam.bt_tech = GLP_BT_BLB; break;
case 3: iParam.bt_tech = GLP_BT_BPH; break;
default: mexErrMsgTxt("glpk: backtrack bad param");
}
if ( glpRealParam[8] > 0.0 && glpRealParam[8] < 1.0 )
iParam.tol_int = glpRealParam[8]; // absolute tolorence
else
mexErrMsgTxt("glpk: tolint must be between 0 and 1");
iParam.tol_obj = glpRealParam[9]; // relative tolarence
iParam.mip_gap = glpRealParam[10]; // realative gap tolerance
// set time limit for mip
if ( glpRealParam[6] < 0.0 || glpRealParam[6] > 1e6 )
iParam.tm_lim = INT_MAX;
else
iParam.tm_lim = (int)(1000.0 * glpRealParam[6] );
// Choose Cutsets for mip
// shut all cuts off, then start over....
iParam.gmi_cuts = GLP_OFF;
iParam.mir_cuts = GLP_OFF;
iParam.cov_cuts = GLP_OFF;
iParam.clq_cuts = GLP_OFF;
switch( glpIntParam[17] ) {
case 0: break;
case 1: iParam.gmi_cuts = GLP_ON; break;
case 2: iParam.mir_cuts = GLP_ON; break;
case 3: iParam.cov_cuts = GLP_ON; break;
case 4: iParam.clq_cuts = GLP_ON; break;
case 5: iParam.clq_cuts = GLP_ON;
iParam.gmi_cuts = GLP_ON;
iParam.mir_cuts = GLP_ON;
iParam.cov_cuts = GLP_ON;
iParam.clq_cuts = GLP_ON; break;
default: mexErrMsgTxt("glpk: cutset bad param");
}
switch( glpIntParam[18] ) { // pre-processing for mip
case 0: iParam.pp_tech = GLP_PP_NONE; break;
case 1: iParam.pp_tech = GLP_PP_ROOT; break;
case 2: iParam.pp_tech = GLP_PP_ALL; break;
default: mexErrMsgTxt("glpk: pprocess bad param");
}
if (glpIntParam[16]) iParam.presolve=GLP_ON;
else iParam.presolve=GLP_OFF;
if (glpIntParam[19]) iParam.binarize = GLP_ON;
else iParam.binarize = GLP_OFF;
}
else {
/* Choose simplex method ('S')
or interior point method ('T')
or Exact method ('E')
to solve the problem */
switch (lpsolver) {
case 1: method = 'S'; break;
case 2: method = 'T'; break;
case 3: method = 'E'; break;
default:
mexErrMsgTxt("glpk: lpsolver != lpsolver");
longjmp (mark, -1);
}
}
// now run the problem...
int errnum = 0;
switch (method) {
case 'I':
errnum = glp_intopt( lp, &iParam );
errnum += 200; //this is to avoid ambiguity in the return codes.
break;
case 'S':
errnum = glp_simplex(lp, &sParam);
errnum += 100; //this is to avoid ambiguity in the return codes.
break;
case 'T':
errnum = glp_interior(lp, NULL );
errnum += 300; //this is to avoid ambiguity in the return codes.
break;
case 'E':
errnum = glp_exact(lp, &sParam);
errnum += 100; //this is to avoid ambiguity in the return codes.
break;
default: /*xassert (method != method); */
mexErrMsgTxt("glpk: method != method");
longjmp (mark, -1);
}
if (errnum==100 || errnum==200 || errnum==300 || errnum==106 || errnum==107 || errnum==108 || errnum==109 || errnum==209 || errnum==214 || errnum==308) {
// Get status and object value
if (isMIP) {
*status = glp_mip_status (lp);
*fmin = glp_mip_obj_val (lp);
}
else {
if (lpsolver == 1 || lpsolver == 3) {
*status = glp_get_status (lp);
*fmin = glp_get_obj_val (lp);
}
else {
*status = glp_ipt_status (lp);
*fmin = glp_ipt_obj_val (lp);
}
}
// Get optimal solution (if exists)
if (isMIP) {
for (int i = 0; i < n; i++)
xmin[i] = glp_mip_col_val (lp, i+1);
}
else {
/* Primal values */
for (int i = 0; i < n; i++) {
if (lpsolver == 1 || lpsolver == 3)
xmin[i] = glp_get_col_prim (lp, i+1);
else
xmin[i] = glp_ipt_col_prim (lp, i+1);
}
/* Dual values */
for (int i = 0; i < m; i++) {
if (lpsolver == 1 || lpsolver == 3)
lambda[i] = glp_get_row_dual (lp, i+1);
else
lambda[i] = glp_ipt_row_dual (lp, i+1);
}
/* Reduced costs */
for (int i = 0; i < glp_get_num_cols (lp); i++) {
if (lpsolver == 1 || lpsolver == 3)
redcosts[i] = glp_get_col_dual (lp, i+1);
else
redcosts[i] = glp_ipt_col_dual (lp, i+1);
}
}
*time = (clock () - t_start) / CLOCKS_PER_SEC;
size_t tpeak;
glp_mem_usage(NULL, NULL, NULL, &tpeak);
*mem=((double) tpeak) / (1024);
lpx_delete_prob(lp);
return 0;
}
else {
// printf("errnum is %d\n", errnum);
}
lpx_delete_prob(lp);
/* this shouldn't be nessiary with glp_deleted_prob, but try it
if we have weird behavior again... */
glp_free_env();
*status = errnum;
return errnum;
}
void fence_insertert::solve() {
#ifdef HAVE_GLPK
ilpt ilp;
instrumenter.message.statistics() << "po^+ edges considered:"
<< unique << " cycles:" << instrumenter.set_of_cycles.size()
<< messaget::eom;
/* sets the variables and coefficients */
//nb of po+ considered * types of fences (_e)
unsigned i=1;
mip_set_var(ilp, i);
/* sets the constraints */
mip_set_cst(ilp, i);
instrumenter.message.debug() << "3: " << i << messaget::eom;
assert(i-1==constraints_number);
const unsigned const_constraints_number=constraints_number;
const unsigned const_unique=unique;
const unsigned mat_size=const_unique*fence_options*const_constraints_number;
instrumenter.message.statistics() << "size of the system: " << mat_size
<< messaget::eom;
instrumenter.message.statistics() << "# of constraints: "
<< const_constraints_number << messaget::eom;
instrumenter.message.statistics() << "# of variables: "
<< const_unique*fence_options << messaget::eom;
ilp.set_size(mat_size);
#ifdef DEBUG
print_vars();
#endif
/* fills the constraints coeff */
/* tables read from 1 in glpk -- first row/column ignored */
mip_fill_matrix(ilp, i, const_constraints_number, const_unique);
instrumenter.message.statistics() << "i: " << i << " mat_size: " << mat_size
<< messaget::eom;
//assert(i-1==mat_size);
#ifdef DEBUG
for(i=1; i<=mat_size; ++i)
instrumenter.message.debug() << i << "[" << ilp.imat[i] << ","
<< ilp.jmat[i] << "]=" << ilp.vmat[i] << messaget::eom;
#endif
/* solves MIP by branch-and-cut */
ilp.solve();
#ifdef DEBUG
print_vars();
#endif
/* checks optimality */
switch(glp_mip_status(ilp.lp)) {
case GLP_OPT:
instrumenter.message.result() << "Optimal solution found"
<< messaget::eom;
break;
case GLP_UNDEF:
instrumenter.message.result() << "Solution undefined" << messaget::eom;
assert(0);
case GLP_FEAS:
instrumenter.message.result() << "Solution feasible, yet not proven \
optimal, due to early termination" << messaget::eom;
break;
case GLP_NOFEAS:
instrumenter.message.result()
<< "No feasible solution, the system is UNSAT" << messaget::eom;
assert(0);
}
event_grapht& egraph=instrumenter.egraph;
/* loads results (x_i) */
instrumenter.message.statistics() << "minimal cost: "
<< glp_mip_obj_val(ilp.lp) << messaget::eom;
for(unsigned j=1; j<=const_unique*fence_options; ++j)
{
if(glp_mip_col_val(ilp.lp, j)>=1)
{
/* insert that fence */
assert(map_to_e.find(col_to_var(j))!=map_to_e.end());
const edget& delay = map_to_e.find(col_to_var(j))->second;
instrumenter.message.statistics() << delay.first << " -> "
<< delay.second << " : " << to_string(col_to_fence(j))
<< messaget::eom;
instrumenter.message.statistics() << "(between "
<< egraph[delay.first].source_location << " and "
<< egraph[delay.second].source_location << messaget::eom;
fenced_edges.insert(std::pair<edget,fence_typet>(delay, col_to_fence(j)));
}
}
#else
throw "Sorry, musketeer requires glpk; please recompile\
musketeer with glpk.";
#endif
}
int DBWorker::_RememberRun(CMyProblem &P, int idobjectives, const char* modelfile, double time, bool mip, int idruns)
{
int idrun = -1;
try {
sql::PreparedStatement *PrepStmt;
sql::ResultSet *res;
PrepStmt = con->prepareStatement(
"INSERT INTO runs(idobjectives,modelfile,runtype,res_status,res_value,time_in_seconds,idruns) VALUES(?,?,?,?,?,?,?)"
);
PrepStmt->setInt(1, idobjectives);
PrepStmt->setString(2, modelfile);
PrepStmt->setString(3, mip ? "mip" : "lp");
if(!mip)
{
PrepStmt->setInt(4, glp_get_status(P.GetProblem()));
PrepStmt->setDouble(5, glp_get_obj_val(P.GetProblem()));
PrepStmt->setNull(7,0);
}
else
{
PrepStmt->setInt(4, glp_mip_status(P.GetProblem()));
PrepStmt->setDouble(5, glp_mip_obj_val(P.GetProblem()));
PrepStmt->setInt(7,idruns);
}
PrepStmt->setDouble(6, time);
PrepStmt->execute();
delete PrepStmt;
if(!mip)
{
PrepStmt = con->prepareStatement(
"SELECT LAST_INSERT_ID()"
);
res = PrepStmt->executeQuery();
delete PrepStmt;
res->next();
idrun = res->getInt(1);
delete res;
}
else
{
idrun = idruns;
}
cout << "Run ID " << idrun << endl;
PrepStmt = con->prepareStatement(
"INSERT INTO results(idrun,var_name,i,j,value,runtype) VALUES(?,?,?,?,?,?)"
);
PrepStmt->setInt(1, idrun);
PrepStmt->setString(6, mip ? "mip" : "lp");
vector<vector<double>> arr;
for (int vvv=1; vvv>=0; vvv--)
{
char* var_name = (vvv ? "x" : "y");
GetSolArray(P.GetProblem(),var_name,arr,mip);
PrepStmt->setString(2, var_name);
int i=1;
for (std::vector<vector<double>>::iterator it = arr.begin() ; it != arr.end(); ++it)
{
int j=1;
for (std::vector<double>::iterator it2 = (*it).begin() ; it2 != (*it).end(); ++it2)
{
PrepStmt->setInt(3, i);
PrepStmt->setInt(4, j);
PrepStmt->setDouble(5, *it2);
PrepStmt->execute();
j++;
}
i++;
}
}
delete PrepStmt;
}
catch (sql::SQLException &e) {
SQLError(e);
}
return idrun;
}
double solve_glp_grb(glp_prob *mip, wrapper_params *par){
GLPK_out = par->glp_out;
GRB_out = par->grb_out;
double obj_val;
/** GLPK: Generate Variable indexing **/
glp_create_index(mip);
/** GLPK: Generate LP **/
glp_write_mps(mip, GLP_MPS_FILE, NULL, "tmp.mps");
/************/
/** GUROBI **/
/************/
retGRB = GRBloadenv(&env, NULL);
if (retGRB || env == NULL)
{
fprintf(stderr, "Error: could not create environment\n");
exit(1);
}
retGRB = GRBsetintparam(env, "OutputFlag", GRB_out?1:0);
if (retGRB) freeMem();
//retGRB = GRBsetintparam(env, "Sensitivity", 1);
//if (retGRB) freeMem();
/** GUROBI: Read model **/
retGRB = GRBreadmodel(env, "tmp.mps", &model);
if (retGRB) freeMem();
/** Remove utility files from disk **/
//remove("tmp.mps");
/** GUROBI: Get environment **/
mipenv = GRBgetenv(model);
if (!mipenv) freeMem();
/** GUROBI: Set parameters **/
/** GUROBI: Ask for more precision **/
retGRB = GRBsetdblparam(mipenv, "FeasibilityTol", 10E-6);
if (retGRB) freeMem();
retGRB = GRBsetdblparam(mipenv, "IntFeasTol", 10E-5);
if (retGRB) freeMem();
retGRB = GRBsetdblparam(mipenv, "MIPgap", 10E-6);
if (retGRB) freeMem();
/* * Playing with gurobi parameters and attr*/
//gurobi_set_basis();
retGRB = GRBsetintparam(mipenv, "Cuts", 3);
if (retGRB) freeMem();
retGRB = GRBsetintparam(mipenv, "RootMethod", 1);
if (retGRB) freeMem();
retGRB = GRBsetintparam(mipenv, "Symmetry", -1);
if (retGRB) freeMem();
/** GUROBI: get numvars and numrows **/
retGRB = GRBgetintattr(model, "NumVars", &numvars);
if (retGRB) freeMem();
/** Test variable names */
for(int j=0;j<numvars;j++){
retGRB = GRBgetstrattrelement(model, "VarName", j, &nameGRB);
printf("GRB Var %d Name %s\n",j,nameGRB);
}
/** GUROBI: get model type **/
retGRB = GRBgetintattr(model, "IsMIP", &GRB_IsMIP);
if (retGRB) freeMem();
/** GUROBI: Optimize model **/
retGRB = GRBoptimize(model);
if (retGRB) freeMem();
/** GUROBI: Retreive the optimization status **/
GRBgetintattr(model, "Status", &retGRB);
switch(retGRB){
case GRB_OPTIMAL:
break;
case GRB_INFEASIBLE :
fprintf(stderr, "Error GRB optimization failed with code GRB_INFEASIBLE\n");
case GRB_INF_OR_UNBD :
fprintf(stderr, "Error GRB optimization failed with code GRB_INF_OR_UNBD \n");
case GRB_UNBOUNDED :
fprintf(stderr, "Error GRB optimization failed with code GRB_UNBOUNDED \n");
case GRB_CUTOFF :
fprintf(stderr, "Error GRB optimization failed with code GRB_CUTOFF \n");
case GRB_ITERATION_LIMIT :
fprintf(stderr, "Error GRB optimization failed with code GRB_ITERATION_LIMIT \n");
case GRB_NODE_LIMIT :
fprintf(stderr, "Error GRB optimization failed with code GRB_NODE_LIMIT \n");
case GRB_TIME_LIMIT :
fprintf(stderr, "Error GRB optimization failed with code GRB_TIME_LIMIT \n");
case GRB_SOLUTION_LIMIT :
fprintf(stderr, "Error GRB optimization failed with code GRB_SOLUTION_LIMIT \n");
case GRB_INTERRUPTED :
fprintf(stderr, "Error GRB optimization failed with code GRB_INTERRUPTED \n");
case GRB_SUBOPTIMAL :
fprintf(stderr, "Error GRB optimization failed with code GRB_SUBOPTIMAL \n");
case GRB_NUMERIC :
fprintf(stderr, "Error GRB optimization failed with code GRB_NUMERIC \n");
/** GUROBI: Quit in any case non optimal **/
freeMem();
}
/** GUROBI: Get obj function value **/
retGRB = GRBgetdblattr(model, "IntVio", &tmp);
if (retGRB) freeMem();
retGRB = GRBgetdblattr(model, "ObjBound", &bound);
if (retGRB) freeMem();
retGRB = GRBgetdblattr(model, "ObjVal", &tmp);
if (retGRB) freeMem();
/* ********************** */
obj_val = tmp;
/* ************ */
if (verbose) printf ("Objective %lf\n", tmp);
if (verbose) printf ("Best bound %lf\n", bound);
if (verbose) printf ("Absolute gap %lf\n", fabs(tmp - bound));
/** GUROBI: Get variable values **/
for (j = 0; j < numvars; ++j){
retGRB = GRBgetdblattrelement(model, "X", j, &tmp);
if (retGRB) freeMem();
retGRB = GRBgetstrattrelement(model, "VarName", j, &nameGRB);
printf("GRB Var %d Name %s\n",j,nameGRB);
if (retGRB) freeMem();
retGRB = GRBgetcharattrelement(model, "VType", j, &type);
if (retGRB) freeMem();
/** GLPK search variable index by name **/
col_index = glp_find_col(mip, nameGRB);
if (col_index != 0){
/** GLPK set variable bounds **/
if ((type == 'B') || (type == 'I')){
if (verbose) printf ("Variable %s is of type %c value %lf fixed to %lf\n", nameGRB, type, tmp, round(tmp));
glp_set_col_bnds(mip, col_index, GLP_FX, round(tmp), round(tmp));
}
else{
if (verbose) printf ("Variable %s is of type %c value %lf fixed to %lf\n", nameGRB, type, tmp, tmp);
glp_set_col_bnds(mip, col_index, GLP_FX, tmp, tmp);
}
}
}
if (GRB_IsMIP){
/** GLPK initialize parameters **/
iparm = (glp_iocp*) malloc(sizeof(glp_iocp));
glp_init_iocp(iparm);
iparm->presolve = GLP_ON;
iparm->mip_gap = glpk_iparm_mip_gap;
iparm->tol_int = glpk_iparm_tol_int;
iparm->tol_obj = glpk_iparm_tol_obj;
/** GLPK get the optimal integer solution **/
ret = glp_intopt(mip, iparm);
if (ret){
fprintf(stderr, "glp_intopt, Error on optimizing the model : %d \n", ret);
freeMem();
}
ret = glp_mip_status(mip);
switch (ret){
case GLP_OPT:
break;
case GLP_FEAS:
fprintf(stderr, "Error GLPK simplex is not optimal, GLP_FEAS, code %d\n", ret);
freeMem();
case GLP_NOFEAS:
fprintf(stderr, "Error GLPK simplex is not optimal, GLP_NOFEAS, code %d\n", ret);
freeMem();
case GLP_UNDEF:
fprintf(stderr, "Error GLPK simplex is not optimal, GLP_UNDEF, code %d\n", ret);
freeMem();
}
}
else{
/*GLPK initialize parameters */
parm = (glp_smcp*) malloc(sizeof(glp_smcp));
glp_init_smcp(parm);
parm->meth = GLP_DUALP;
parm->tol_bnd = 10E-4;
parm->tol_dj = 10E-4;
/* GLPK get the optimal basis */
//ret = glp_simplex(mip, parm);
if (ret){
fprintf(stderr, "glp_simplex, Error on optimizing the model : %d \n", ret);
freeMem();
}
ret = glp_get_status(mip);
switch (ret){
case GLP_OPT:
break;
case GLP_FEAS:
fprintf(stderr, "Error GLPK simplex is not optimal, GLP_FEAS, code %d\n", ret);
freeMem();
case GLP_INFEAS:
fprintf(stderr, "Error GLPK simplex is not optimal, GLP_INFEAS, code %d\n", ret);
freeMem();
case GLP_NOFEAS:
fprintf(stderr, "Error GLPK simplex is not optimal, GLP_NOFEAS, code %d\n", ret);
freeMem();
case GLP_UNBND:
fprintf(stderr, "Error GLPK simplex is not optimal, GLP_UNBND, code %d\n", ret);
freeMem();
case GLP_UNDEF:
fprintf(stderr, "Error GLPK simplex is not optimal, GLP_UNDEF, code %d\n", ret);
freeMem();
}
}
//GRBmodel *fmod = fixed_model(model);
//gurobi_sens_output(fmod, "/tmp/sens.sol");
GRBwrite(model, "/tmp/model.sol");
/** GUROBI: free structures **/
if (model) GRBfreemodel(model);
if (env) GRBfreeenv(env);
return obj_val;
}
